Abstract
This study compared the performance of response surface methodology (RSM) and artificial neural network (ANN) in modelling Colocynthis Vulgaris shrad peel (CVSP) hydrolysis using H2SO4. The characterization results from the proximate analysis, FTIR, SEM, EDX, XRF and XRD indicated that CVSP contains a significant amount of hydrolyzable cellulose. Pre-treatment temperature, time and acid concentration are the independent factors, and fermentable sugar yield, the response. Assessment of the models through the coefficient of determination (R2) and mean square error (MSE) indicates that ANN (R2=0.9999, MSE=8.7277E-12) has a higher predictive potential compared to the RSM model (R2=0.965264, MSE=5.39) in predicting the process. However, both models predictions were adequate and in good agreement with experimental data.
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